Intraoperative refractory status epilepticus caused by propofol -a case report-

Kumar Abhyuday,Kumar Amarjeet,Kumar Neeraj,Kumar Ajeet 대한마취통증의학회 2021 Korean Journal of Anesthesiology Vol.74 No.1

Background: Status epilepticus, when continued despite the administration of two antiepileptic drugs, is called refractory status epilepticus (RSE). The seizure-like phenomenon due to propofol is widely reported in the literature. However, RSE caused by propofol is rare and is a diagnostic dilemma. Case: A 44-year-old male patient presented with RSE during the intraoperative period and was under general anesthesia on propofol infusion. The seizure was resistant to benzodiazepines and phenytoin. Thereafter, the seizure subsided after the discontinuation of propofol infusion, and the patient was shifted to fentanyl and dexmedetomidine infusion for the maintenance of anesthesia. The postoperative follow-up was uneventful. Conclusions: This article focuses on the management of intractable intraoperative seizure and highlights the need for the exploration of seizure characteristics caused by propofol.

Changes in patterns of plastic surgery emergencies at a level I trauma center in India during the COVID-19 pandemic

( Veena Singh ),( Ansarul Haq ),( Sarsij Sharma ),( Sanjeev Kumar ),( Aditya Kumar ),( Amarjeet Kumar ),( Neeraj Kumar ),( Anil Kumar ) 대한외상학회 2022 大韓外傷學會誌 Vol.35 No.2

Purpose: The coronavirus disease 2019 (COVID-19) pandemic has had major effects worldwide, including sudden and forceful setbacks to the healthcare system. The COVID-19 pandemic has also led to changes in the plastic and reconstructive management of emergency cases, including those due to road traffic accidents. This study analyzed changes in patterns of plastic surgery emergencies and modifications in consultation policies to minimize the exposure of healthcare workers. Methods: Data on plastic surgery emergency calls received from the trauma and emergency department were collected for a period of 2 months before and during lockdown. The data were then analyzed with respect to the cause, mechanism, and site of the injury, as well as other variables. Results: During lockdown, there was a 40.4% overall decrease in the plastic surgery emergency case volume (168 vs. 100). The average daily number of consultations before lockdown was 2.8 as compared to 1.6 during lockdown. Road traffic accidents remained the most common mechanism of injury in both groups (45.8% vs. 39.0%) but decreased in number during the lockdown (77 vs. 39). Household accidents, including burns, were the second most common cause of injury in both phases (7.7% vs. 20.0%), but their proportion increased significantly from 7.7.% to 20.0% in the lockdown phase (P=0.003). The percentage of minor procedures done in the emergency department increased from 53.5% to 72.0% during lockdown (P=0.002). Procedures in the operating room decreased by 73.1% during lockdown (67 vs. 18, P=0.001). Conclusions: The COVID-19 pandemic and lockdown orders in India greatly influenced trends in traumatic emergencies as observed by the plastic surgery team at our tertiary care center. Amidst all the chaos and limitations of the pandemic period, providing safe and prompt care to the patients presenting to the emergency room was our foremost priority.

Investigations of Ferroelectric Polarization Switching in Potassium Nitrate Composite Films

Neeraj Kumar,Rabinder Nath 한국전기전자재료학회 2014 Transactions on Electrical and Electronic Material Vol.15 No.2

This article explains the experimental results of ferroelectric polarization switching (FPS) of potassium nitrate (KNO3)with different polymers such as polyvinylidene fluoride (PVDF) and polyvinyl fluoride (PVF) using simple melt-presstechniques. To analyze the ferroelectric polarization switching in potassium nitrate (KNO3) composite films at roomtemperature, we applied the Ishibashi and Takagi theory (based on Avrami model) to the switching current transient. To investigate the dimensionality of domain growth, the ferroelectric polarization switching current (FPS current)was observed from the square - wave bipolar signals across a resistance of 0.1 kΩ in series with the composite films. The existence of a switching current transient pulse confirmed the ferroelectricity and indicated the stability of theferroelectric phase (phase III) of KNO3 at room temperature. Polarization hysteresis (P-E) characteristics supportedthe prominent features of ferroelectric polarization switching in the composite films at room temperature.

Molecular Markers and Their Usefulness in Rice Breeding

( Neeraj Kumar Tyagi ),( Bandarupalli Ramesh ),( Kuldeep Tyagi ) 전북대학교 농업과학기술연구소 2009 농업생명과학연구 Vol.40 No.2

Molecular markers are extensively used for improving and sustaining the rice productivity. A variety of molecular genetic markers, including restriction fragment length polymorphisms (RFLPs), random amplified polymorphic DNAs (RAPDs), amplified fragment length polymorphisms (AFLPs), microsatellites or simple sequence repeats (SSRs), expressed sequence tags (EST) and single nucleotide polymorphism (SNP) have been developed providing new tools for rice breeding. The major advantages of the molecular markers over the other classes of markers are their number is potentially unlimited, spanning across the genome, their expression is unaffected by the environment and their assessment is independent of the stage of plant development. Molecular markers are landmarks in the chromosome maps that can be used to monitor the transfer of specific chromosome segments known to carry useful agronomic traits. Breeders use these molecular markers to increase the precision of selection for the best trait combinations. Molecular markers have large number of applications ranging from diversity analysis to the improvement of rice varieties by marker assisted selection. This review describes the usefulness of some important DNA markers in rice improvement.

Bayesian Coalition Negotiation Game as a Utility for Secure Energy Management in a Vehicles-to-Grid Environment

Kumar, Neeraj,Misra, Sudip,Chilamkurti, Naveen,Jong-Hyouk Lee,Rodrigues, Joel J. P. C. IEEE 2016 IEEE transactions on dependable and secure computi Vol.13 No.1

<P>In recent times, Plug-in Electric Vehicles (PEVs) have emerged as a new alternative to increase the efficiency of smart grids (SGs) in a vehicles-to-grid (V2G) environment. The V2G environment provides a bidirectional power and information flow, so that users can have an optimized usage as per their requirements. However, uncontrolled and unmanaged power distribution may lead to an overall performance degradation in V2G environment. One reason for this uncontrolled and unmanaged flow may be due to the usage of power by unauthorized users. To address this issue, we propose a Bayesian Coalition Negotiation Game (BCNG) as a utility for secure energy management for PEVs in the V2G environment. We have used a BCNG along with Learning Automata (LA), wherein LA are stationed on PEVs and are assumed as the players in the game. To provide an approach based on resilience for any misuse of electricity consumption, a new Secure Payoff Function (SPF) is proposed. The players take actions and update their action probability vector using the SPF. A Nash Equilibrium (NE) is also achieved in the game using convergence theory. Our proposal is evaluated with various metrics. The proposed scheme also provides mutual authentication and resilience against various attacks during power distribution.</P>

Influence of Inhibitors on the Corrosion of Al and Al-composites in Chloride-containing Solutions - A Review

Neeraj Kumar,Ashok K. Srivastava,Prabhat Gautam,M. K. Manoj 한국재료학회 2022 한국재료학회지 Vol.32 No.5

Corrosion is a natural, inevitable process, and is one of the world's most serious problems. Losses incurred due to corrosion are extremely expensive for society. Several technological strategies have been explored and implemented to address these losses. The use of inhibitors to prevent corrosion is a common and efficient method to reduce corrosion losses. This review covers Al and Al-composite corrosion inhibitors in chloride-containing solutions, because of their popularity in a broad array of industrial applications. A vast number of studies in the literature detail the common tendency of Al and Al-composites with reinforcements to deteriorate. Accordingly, it is worthwhile to employ inhibitors to protect them, as discussed in the present work. The emphasis is on selecting the smartest corrosion inhibitor and evaluating its performance. According to the study, the most commonly used corrosion inhibitors are 1,4-naphthoquinone (NQ), 1,5-naphthalene diol, 3-amino-1,2,4-triazole-5-thiol (ATAT), ammonium tetrathiotungstate, clotrimazole, amoxicillin, antimicrobial and antifungal drugs. Electrochemical impedance spectroscopy (EIS), potentiodynamic (PDP), and weight loss were among the most commonly used modern electrochemical technologies to test inhibitors’ efficacy under environmental conditions.

Offline Handwritten Gurmukhi Character Recognition : A Review

Neeraj Kumar,Sheifali Gupta 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.5

All over India more than 12 crore people utilize Gurumukhi script for speaking, documenting & other purposes. A considerable advancement in the work associated with the recognition of handwritten and printed Gurmukhi text has been reported in last few years. From the last few decades offline handwritten character recognition has gained a lot of interest of researchers. It is well known that each individual has some different writing style, so it is very difficult to identify or recognize the handwritten characters. Researchers have worked in this field using various scripts like Hindi, English but a very little work has been done in Gurmukhi script point of view. Based on data acquirement process a concise classification of recognition system has been discussed in this article. Various feature mining techniques & classifiers like power arc fitting ,parabola arc fitting, ,diagonal feature extraction, transition feature extraction, K-NN classifier (K-nearest neighbor) & SVM classifier (Support vector machine) are also illustrated in this paper. The methodology for word recognition has also been discussed in this paper.

Collaborative-Learning-Automata-Based Channel Assignment With Topology Preservation for Wireless Mesh Networks Under QoS Constraints

Kumar, Neeraj,Jong-Hyouk Lee IEEE 2015 IEEE systems journal Vol.9 No.3

<P>Wireless mesh networks have emerged as a new technology for providing cost-effective broadband Internet access to users living in different communities across the globe. However, due to changes in a network topology across different paths, it is a challenging task to handle heavy data traffic in a multichannel environment. To address this issue, we propose a new collaborative-learning-automata-based channel assignment with topology preservation in this paper. In the proposed scheme, learning automata (LA) are deployed at the nearest mesh routers to collaborate with each other for information sharing and data transmission while learning from an environment. For each performed action, the LA get a reward or a penalty from the environment. Based on the inputs from the environment, the LA update their action probability vector and then decide the next action. The performance of the proposed scheme is evaluated with respect to various metrics such as throughput, data delivery ratio, switching and buffering delays, effective transmission, and effective channel utilization.</P>

Low Field Magneto-transport and Thermal Studies on (1-x) Pr<SUB>2/3</SUB>Ba<SUB>1/3</SUB>MnO₃+x PdO Composite Manganites

Neeraj Panwar,Neeraj Kumar,Vikram Sen,D. K. Pandya,S. K. Agarwal 한국자기학회 2008 한국자기학회 학술연구발표회 논문개요집 Vol.- No.-

Comparative Study to Measure the Performance of Commonly Used Machine Learning Algorithms in Diagnosis of Alzheimer's Disease

kumar, Neeraj,manhas, Jatinder,sharma, Vinod Korea Multimedia Society 2019 The journal of multimedia information system Vol.6 No.2

In machine learning, the performance of the system depends upon the nature of input data. The efficiency of the system improves when the behavior of the input data changes from un-normalized to normalized form. This paper experimentally demonstrated the performance of KNN, SVM, LDA and NB on Alzheimer's dataset. The dataset undertaken for the study consisted of 3 classes, i.e. Demented, Converted and Non-Demented. Analysis shows that LDA and NB gave an accuracy of 89.83% and 88.19% respectively in both the cases whereas the accuracy of KNN and SVM improved from 46.87% to 82.80% and 53.40% to 88.75% respectively when input data changed from un-normalized to normalized state. From the above results it was observed that KNN and SVM show significant improvement in classification accuracy on normalized data as compared to un-normalized data, whereas LDA and NB reflect no such change in their performance.